Metadata-Version: 2.1
Name: consmodel
Version: 0.2.0
Summary: The library aims to provide a simple way to create individual consumer loads, generation.
Home-page: https://github.com/blazdob/consmodel
Author: Blaž Dobravec
Author-email: blaz@dobravec.si
Project-URL: Bug Tracker, https://github.com/blazdob/consmodel/issue
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENCE.txt
Requires-Dist: setuptools >=42
Requires-Dist: retry-requests >=2.0.0
Requires-Dist: openmeteo-requests >=1.1.0
Requires-Dist: requests-cache >=1.1.0
Requires-Dist: pvlib >=0.9.1
Requires-Dist: pandas >=1.5.2
Requires-Dist: numpy >=1.22.4
Requires-Dist: tzfpy >=0.15.1
Requires-Dist: meteostat >=1.6.5
Requires-Dist: scipy >=1.10.0
Requires-Dist: hplib ==1.9

# Welcome to consmodel library 👋

![Python Version](https://img.shields.io/badge/3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)

!!! Warning: the library is active and the functionalities are being added on weekly basis, some functionalities will also change !!!

The library aims to provide a simple way to create individual consumer loads and generation.

The library is a centralised modelling tool that implements the following consumption/generation consumptions:
   * pure consumption model,
   * solar plant model,
   * heat pump model,
   * electric vehicle modelling,
   * possibly other models...

The main idea of the library is to be able to easily create consumption or generation power consumption profiles.

The schema of the library is as follows:
![](imgs/diagram.png)
### 🏠 [Homepage](https://github.com/blazdob/consmodel)

## Install

```sh
pip3 install consmodel
```

## Usage

### PV model
```python
   from consmodel import PV
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   # create a simple PV model
   pv = PV(lat=46.155768,
           lon=14.304951,
           alt=400,
           index=1,
           name="test",
           freq="15min",)
   timeseries = pv.simulate(pv_size=14.,
                            year=2022,
                            model="ineichen",
                            consider_cloud_cover=True)
   # plot the results
   timeseries.plot()
   plt.show()
```

### BS model
```python
   from consmodel import BS
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   # create a simple PV model
   test_consumption = [0.,-3.,-2.,8.,7.,6.,7.,8.,5.,4.,-2.]
   test_consumption_df = pd.DataFrame({"p": test_consumption},
                  index=pd.date_range("2020-01-01 06:00:00",
                                       periods=11,
                                       freq="15min"))
   bs = BS(lat=46.155768,
           lon=14.304951,
           alt=400,
           index=1,
           st_type="10kWh_5kW",
           freq="15min",)
   timeseries = batt.simulate(control_type="installed_power",
                              p_kw=test_consumption_df)
   # plot the results
   timeseries.plot()
   plt.show()
```

### Consumer model
```python
   from consmodel import ConsumerModel
   import pandas as pd
   import numpy as np
   import matplotlib.pyplot as plt

   cons = ConsumerModel(lat=46.155768,
                        lon=14.304951,
                        alt=400,
                        index=1,
                        name="ConsumerModel_default",
                        tz="Europe/Ljubljana",
                        use_utc=False,
                        freq="15min",)
   timeseries = cons.simulate(has_generic_consumption=False,
                              has_pv=True,
                              has_heatpump=True,
                              has_ev=False,
                              has_battery=True,
                              start=pd.to_datetime("2020-01-01 06:15:00"),
                              end=pd.to_datetime("2020-01-01 06:00:00")+pd.Timedelta("1d"),
                              pv_size=14.,
                              wanted_temp=20.,
                              hp_st_type="Outdoor Air / Water (regulated)",
                              bs_st_type="10kWh_5kW",
                              control_type="production_saving")
   timeseries.plot()
   plt.show()
```


## Author

👤 **Blaž Dobravec**

* Website: https://github.com/blazdob
* Github: [@blazdob](https://github.com/blazdob)
* LinkedIn: [@https:\/\/www.linkedin.com\/in\/blaz-dobravec\/](https://linkedin.com/in/https:\/\/www.linkedin.com\/in\/blaz-dobravec\/)

## Colaborated:

* Matej Oblak: [@MatejGitOblak](https://github.com/MatejGitOblak)
* Bine Flajnik: [@Bine-f](https://github.com/Bine-f)


## 🤝 Contributing

Contributions, issues and feature requests are welcome!

Feel free to check [issues page](https://github.com/blazdob/consmodel/issue).

## Show your support

Give a ⭐️ if this project helped you!

